Current through 2024-13, March 27, 2024
Methods described in this section are used to make decisions
about classification attainment. The models are constructed to sequentially
amass evidence concerning the highest level of classification criteria that a
test community attains, using quantitative predictor variables defined in
Section
3(C). The pertinent
question, in terms of the classification attainment, is whether or not a test
community is attaining at least its statutory classification. The methods
described in this rule may also be used to determine if a given waterbody
attains a higher class and therefore may be subject to statutory
antidegradation provisions or considered for water quality reclassification.
The methods may also be used, where appropriate, for other purposes including
assessment of pre-impact baseline conditions or site-specific impact
evaluations.
A.
General
provisions for aquatic life standards. Except as otherwise provided in
Section 3(G)(3), Professional judgement, of this chapter, all samples of
benthic macroinvertebrates that are collected for the purpose of classification
attainment evaluation using the linear discriminant model described in the
following section, whether collected by the department or by any person
submitting data to the department, must be collected, processed and identified
in conformance with "Methods for Biological Sampling and Analysis of Maine's
Rivers and Streams" (DE P LW 0387 -B2002). Selection of an appropriate sampling
site must also conform to criteria set forth in "Methods for Biological
Sampling and Analysis of Maine's Rivers and Streams" (DE P LW 0387 -B2002).
Quantitative analysis of the sample must conform to the requirements set forth
in Sections 3(B) through 3(F) of this chapter and must include a quality
assurance plan approved by the department, as specified in "Methods for
Biological Sampling and Analysis of Maine's Rivers and Streams" (DE P LW 0387
-B2002). Samples must be identified to the genus level, where practicable.
Computation of indices and measures of community structure required for the
linear discriminant models must be adjusted to the genus level of taxonomy (see
Section 3(C), V ariable number 2, Generic Richness).
Minimum Provisions. Samples that have been properly collected
and analyzed but fail to meet either of the following criteria are unsuitable
for further analysis through the linear discriminant models:
(1) Total mean abundance (Section 3(C)
Variable number 1) must be at least 50 individuals (average
per basket/cone/bag); and
(2)
Generic richness (Section 3(C) Variable number 2) for three replicate
basket/cone/bag samplers must be at least
15.
Samples not attaining these criteria may be evaluated
according to Section 3(G) of this chapter, Professional judgment.
B.
Aquatic life
statistical decision models. The following statistical decision models
consist of linear discriminant functions developed to use quantitative
ecological attributes of the macroinvertebrate community (see Section 3(C)
through 3(E) ) to determine the strength of the association of a test community
to any of the water quality classes (Appendix 1).
The coefficients or weights (see Section 3(F) ) are
calculated using a linear optimization algorithm to minimize the distance, in
multivariate space, between sites within a class, and to maximize the distance
between sites between classes. The linear discriminant function has the
form:
Z = C + W1X1 +
W2X2 +
...WnXn
Where: Z = discriminant score
C = constant
Wi = the coefficients or weights (from
Section 3(F) )
Xi = the predictor variable values
(from Section 3(C) )
Association values are computed, using variable values from a
test sample, for each classification by employing one four-way model and three
two-way models. The four-way model uses nine variables pertinent to the
evaluation of all classes and provides four initial probabilities that a given
site attains one of three classes (AA/A, B, or C), or is in nonattainment (NA)
of the minimum criteria for any class. Class AA and Class A have the same
aquatic life standards and, therefore, are treated as the same aquatic life
class. These probabilities have a possible range from 0.0 to 1.0, and are used,
after transformation, as variables in each of the three subsequent final
decision models. The final decision models (the three, two-way models) are
designed to distinguish between a given class and any higher classes as one
group and any lower classes as the other group (e.g., Classes AA/A+B+C vs. NA;
Classes AA/A+B vs. Class C+NA; Class AA/A vs. Classes B+C+NA). The equations
for the final decision models use the predictor variables relevant to the class
being tested (Section 3(F) ). The resultant discriminant scores are known as
the Mahalonobis Distance where:
Mahalonobis Distance = Zt (sample x) =
g1 (x,t) + g2 (t)
Where: Zt = discriminant score for
sample x, class t
g1 (x,t) =
(x-mt)' S-1
(x-mt)
g2 (t) = -2
loge (qt) = 0 (if prior
probabilities are equal)
Where: x = a vector containing all the values of all the
variables for a given linear discriminant function, for a given sample, of
class t
mt = a vector, as for x, but
containing the means of all predictor variables in the given linear
discriminant function, for the given sample, of class t
S = pooled covariance matrix (the variance of the
multivariate observation)
qt = value of the prior probability
that a given sample is Class A, B, C, or NA.
The probability (association value) of a sample x, belonging
to a particular class t, is:
Click
here to view Image
Where: Pt(x) = the probability that
sample x belongs to class t (for Classes A, B, C, NA)
e = the exponential function
-0.5 = a standardization constant from the normal
distribution
Zt = the discriminant score or
Mahalonobis Distance for class t (Classes A, B, C, NA)
C.
Methods for the calculation of
indices and measures used in the linear discriminant models Variables (1) to
(30) are as follows.
(1) Total mean
abundance. Count all individuals in all replicate samplers from a site and
divide by the number of replicates to yield the mean number of individuals per
sampler.
(2) Generic richness.
Count the number of different genera found in all replicate samplers from one
site.
Counting rules for generic richness:
(a) Species-level counts. All population
counts at the species level are aggregated to the generic level.
(b) Family-level counts, no more than one
genus. A family level identification that includes no more than one taxon
identified to the generic level is counted as a separate taxon in generic
richness counts.
(c) Family-level
counts, more than one genus. A family level identification with more than one
taxon identified to generic level is not counted toward generic richness.
Counts are divided proportionately among the genera that are present.
(d) Phylum, Class, or Order counts. A higher
level taxonomic identification (Phylum, Class, Order) is not counted toward
generic richness unless it is the only representative.
(e) Pupae. Pupae are ignored in all
calculations.
(3)
Plecoptera mean abundance. Count all individuals from the order Plecoptera in
all replicate samplers from one site and divide by the number of replicates to
yield mean number of Plecopteran individuals per sampler.
(4) Ephemeroptera mean abundance. Count all
individuals from the order Ephemeroptera in all replicate samplers from one
site and divide by the number of replicates to yield the mean number of
Ephemeropteran individuals per sampler.
(5) Shannon-Wiener Generic Diversity.
Shannon-Wiener generic diversity is computed after adjusting all counts to
genus, as described under paragraph (2) above.
Click
here to view Image
where: Click here to view
Image= Shannon-Wiener Diversity
c = 3.321928 (converts base 10 log to base 2)
N = Total abundance of individuals
ni = Total abundance of individuals in
the ith taxon
(6) Hilsenhoff Biotic Index. HBI is
calculated using all taxa in the sample that have assigned tolerance values.
Tolerance values are provided in Hilsenhoff, William. 1987. An Improved Biotic
Index of Organic Stream Pollution,
The Great Lakes
Entomologist 20:31-39.
Click
here to view Image
Where: HBI = Hilsenhoff Biotic Index
Ni = number of individuals in the
ith taxon
aI = tolerance value assigned to that
taxon
N = total number of individuals in sample with tolerance
values
(7) Relative
Chironomidae abundance. Calculate the mean number of individuals of the family
Chironomidae, following the counting rules in Variable 4, and divide by total
abundance (Variable 1).
(8)
Relative Diptera richness. Count the number of genera of the Order Diptera,
following counting rules in Variable 2, and divide by generic richness
(Variable 2).
(9)
Hydropsyche abundance. Count all the individuals from the
genus Hydropsyche in all replicate samplers from a site, and
divide by the number of replicates to yield mean number of
Hydropsyche individuals per sampler.
(10) Probability (A+B+C) from first stage
model. The sum of probabilities for Classes A, B, and C from first stage
model.
(11)
Cheumatopsyche abundance. Count all individuals from the genus
Cheumatopsyche in all replicate samplers from one site and
divide by the number of replicates to yield mean number of
Cheumatopsyche individuals per sampler.
(12) EPT-Diptera richness ratio. Divide EPT
generic richness (Variable 19) by the number of genera from the order Diptera,
following counting rules in Variable 2. If the number of genera of Diptera in
the sample is 0, a value of 1 is assigned to the denominator.
(13) Relative Oligochaeta abundance.
Calculate the mean number of individuals of the class Oligochaeta, following
counting rules in Variable 4, and divide by total abundance (Variable
1).
(14) Probability (A+B) from
first stage model. The sum of probabilities for Classes A and B from first
stage model.
(15) Perlidae mean
abundance. Count all individuals from the family Perlidae (Section 3(E) ) in
all replicate samplers from one site and divide by the number of replicates to
yield mean number of Perlidae per sampler.
(16) Tanypodinae mean abundance. Count all
individuals from the subfamily Tanypodinae (Section 3(E) ) in all replicate
samplers from one site and divide by the number of replicates to yield mean
number of Tanypodinae per sampler.
(17) Chironomini mean abundance. Count all
individuals from the tribe Chironomini (Section 3(E) ) in all replicate
samplers from one site and divide by the number of replicates to yield mean
number of Chironomini per sampler.
(18) Relative Ephemeroptera abundance.
Variable 4 divided by Variable 1.
(19) EPT generic richness. Count the number
of different genera from the order Ephemeroptera (E), Plecoptera (P), and
Trichoptera (T) in all replicate samplers, according to counting rules in
Variable 2, generic richness.
(20)
Variable reserved.
(21) Sum of mean
abundance of Dicrotendipes & Micropsectra
& Parachironomus & Helobdella. Sum
the abundance of the 4 genera and divide by the number of replicates (as
performed in Variable 4).
(22)
Probability of Class A from first stage model.
(23) Relative Plecoptera richness. Count
number of genera of Order Plecoptera, following counting rules in Variable 2,
and divide by generic richness (Variable 2).
(24) Variable reserved.
(25) Sum of mean abundance of
Cheumatopsyche & Cricotopus &
Tanytarsus & Ablabesmyia. Sum the number of
individuals in each genus in all replicate samplers and divide by the number of
replicates (as performed in Variable 4).
(26) Sum of mean abundance of
Acroneuria & Stenonema. Sum the number of
individuals in each genus in all replicate samplers and divide by the number of
replicates (as in Variable 4).
(27)
Variable reserved.
(28) Ratio of EP
generic richness. Count the number of different genera from the order
Ephemeroptera (E), and Plecoptera (P) in all replicate samplers, following
counting rules in Variable 2, and divide by 14 (maximum expected for Class
A).
(29) Variable
reserved.
(30) Ratio of Class A
indicator taxa. Count the number of Class A indicator taxa as listed in Section
3(D) that are present in the community and divide by 7 (total possible
number).
D.
Indicator taxa for Class A
Brachycentrus --- (Trichoptera:
Brachycentridae)
Serratella -------- (Ephemeroptera:
Ephemerellidae)
Leucrocuta ------- (Ephemeroptera:
Heptageniidae)
Glossosoma ------ (Trichoptera:
Glossosomatidae)
Paragnetina ----- (Plecoptera:
Perlidae)
Eurylophella ------ (Ephemeroptera:
Ephemerellidae)
Psilotreta -------- (Trichoptera:
Odontoceridae)
E.
Family functional groups
PLECOPTERA
Perlidae
Acroneuria Agnetina
Attaneuria Beloneuria
Eccoptura Neoperla
Paragnetina Perlesta
Perlinella
CHIRONOMIDAE
Tanypodinae
Ablabesmyia Clinotanypus
Coelotanypus Conchapelopia
Djalmabatista Guttipelopia
Hudsonimyia Labrundinia
Larsia Meropelopia
Natarsia Nilotanypus
Paramerina Pentaneura
Procladius Psectrotanypus
Rheopelopia Tanypus
Telopelopia Thienemannimyia
Trissopelopia Zavrelimyia
Chironomini
Pseudochironomus Axarus
Chironomus Cladopelma
Cryptochironomus Cryptotendipes
Demicryptochironomus Dicrotendipes
Einfeldia Endochironomus
Glyptotendipes Goeldichironomus
Harnischia Kiefferulus
Lauterborniella Microchironomus
Microtendipes Nilothauma
Pagastiella Parachironomus
Paracladopelma Paralauterborniella
Paratendipes Phaenopsectra
Polypedilum Robackia
Stelechomyia Stenochironomus
Stictochironomus Tribelos
Xenochironomus
F.
Model coefficients
First Stage Model
Coefficients
|
Variable number
|
Transformation
|
Class A
|
Class B
|
Class C
|
Nonattainment
|
| Constant |
-99.95508 |
-105.70948 |
-112.67581 |
-107.74283 |
| 1 |
nLog (value +0.001) |
10.77061 |
11.46981 |
11.80888 |
11.26793 |
| 2 |
-0.38619 |
-0.43340 |
-0.50051 |
-0.48822 |
| 3 |
nLog (value +0.001) |
0.23940 |
0.03946 |
-0.60923 |
-0.95480 |
| 4 |
nLog (value +0.001) |
-0.59970 |
-0.55500 |
-0.67722 |
-1.79032 |
| 5 |
21.22732 |
20.91256 |
21.07602 |
19.46547 |
| 6 |
8.01620 |
9.12163 |
10.31492 |
10.72746 |
| 7 |
nLog (value +0.001) |
-11.70298 |
-11.52650 |
-11.49414 |
-11.66371 |
| 8 |
70.77937 |
71.09637 |
72.46514 |
70.22517 |
| 9 |
-0.00535 |
-0.00398 |
-0.00152 |
0.00007 |
Final Classification Models
Class C or better model
Coefficients
|
Variable number
|
Transformation
|
Class A-B-C
|
Nonattainment
|
| Constant |
-25.70020 |
-8.55844 |
| 10 |
Arcsin |
19.98470 |
3.36032 |
| 11 |
nLog (value +0.001) |
-0.26001 |
-0.43781 |
| 12 |
Sq. root |
5.57672 |
5.92732 |
| 13 |
nLog (value +0.001) |
-2.33229 |
-1.89945 |
Class B or better model
Coefficients
|
Variable number
|
Transformation
|
Class A-B
|
Class C-nonattainment
|
| Constant |
-17.81016 |
-6.93836 |
| 14 |
Arcsin |
12.04826 |
3.63707 |
| 15 |
nLog (value +0.001) |
-1.11091 |
-1.03934 |
| 16 |
nLog (value +0.001) |
-0.10582 |
0.01978 |
| 17 |
nLog (value +0.001) |
0.17813 |
0.10825 |
| 18 |
4.03202 |
-1.14508 |
| 19 |
0.87400 |
0.63310 |
| 21 |
nLog (value +0.001) |
-0.69360 |
-0.53194 |
Class A model
Coefficients
|
Variable number
|
Transformation
|
Class A
|
Class B-C-nonattainment
|
| Constant |
-9.59254 |
-4.08552 |
| 22 |
Arcsin |
8.34341 |
1.52080 |
| 23 |
3.78999 |
4.27447 |
| 25 |
nLog (value +0.001) |
0.53110 |
0.77851 |
| 26 |
nLog (value +0.001) |
-0.55838 |
-0.51448 |
| 28 |
12.32529 |
9.81592 |
| 30 |
6.94828 |
-0.67475 |
G.
Professional judgment. Where there is documented evidence of
conditions that could result in uncharacteristic findings, allowances may be
made to account for those situations by adjusting the classification attainment
decision through use of professional judgement, as provided in this section,
paragraphs 3(G)(1) to 3(G)(3). The department may make adjustments to the
classification attainment decision based on analytical, biological, and habitat
information or may require that additional monitoring of affected waters be
conducted prior to issuing a classification attainment decision.
(1) Sampling procedures and minimum
provisions conform but other confounding factors exist. When samples of test
communities conform to criteria provided in "Methods for Biological Sampling
and Analysis of Maine's Rivers and Streams" (DE P LW 0387 -B2002) and Section
3(A) of this chapter, they are suitable to be analyzed by the linear
discriminant models for classification attainment evaluation. These models are
not suitable for use in areas of impoundments that thermally stratify or in
areas where there is a net annual deposition of fine sediment. Professional
judgement may be utilized when conditions are found that are atypical to the
derivation of the linear discriminant model, as provided in Section 3(B-F).
Factors that may allow adjustments to the model outcome include but are not
limited to: habitat factors, including lake outlets from waters classified GPA,
unusual naturally-caused substrate character, tidal effects, cataclysmic
events, oligotrophic conditions; sampling factors, including disturbed samples,
unusual taxa assemblages, and documented human error in sampling; and sample
processing factors, including subsample vs. whole sample analysis and
documented human error in processing. The following adjustments may be made to
correct for these conditions:
(a) Raise the
finding. On the basis of documented evidence of specific conditions such as
those defined above, the department may decide:
(i) To raise the classification attainment
outcome predicted by the model from nonattainment of any class to indeterminate
or to attainment of Class C; or
(ii) To raise the classification attainment
outcome predicted by the model from attainment in one class to attainment in
the next higher class; or
(iii) To
determine that a sample with an indeterminate outcome for a given class attains
that class.
(b) Lower
the finding. On the basis of documented, substantive evidence that the
narrative aquatic life criteria for the assigned class are not met, the
department may decide to lower the classification attainment finding.
(c) Indeterminate. Where the department
cannot make a finding as described in 3(G)(1)(a-b), additional monitoring of
the test community may be required before a determination of class attainment
can be made.
(2) Minimum
provisions do not conform. For classification evaluation of test communities
that do not conform to criteria provided in Section 3(A) of this chapter,
minimum provisions, professional judgement may be used as follows:
(a) Determination of nonattainment. Those
samples having any of the ecological attributes not attaining the minimum
provisions (Section 3(A) ), and where there is no evidence of confounding
factors that could result in uncharacteristic findings as defined above
(Section 3(G)(1) ), must be determined to be in nonattainment of the minimum
provisions of the aquatic life criteria for any class.
(b) Determination of attainment when minimum
provisions are not met. Where there is evidence of factors that could result in
minimum provisions not being met, professional judgment may be used to make a
professional finding of attainment of the aquatic life criteria for any class.
Such decisions will be provisional until appropriate resampling is carried
out.
(3) Standard
sampling procedures are not feasible or appropriate. For classification
attainment evaluation of test communities that do not conform to criteria
provided in "Methods for Biological Sampling and Analysis of Maine's Rivers and
Streams" (DE P LW 0387 -B2002), the department may make an assessment of
classification attainment or aquatic life impact in accordance with the
following procedures:
(a) Approved assessment
plan. A quantitative sampling and data analysis plan must be developed in
accordance with methods established in the scientific literature on water
pollution biology, and the department must approve the plan.
(b) Determination of sampling methods.
Sampling methods are determined on a site-specific basis, based on habitat
conditions of the sampling site, and the season sampled;
(i) The preferred method for sampling
hard-bottomed substrates is the rock basket/cone/bag method as described in
"Methods for Biological Sampling and Analysis of Maine's Rivers and Streams"
(DE P LW 0387 -B2002).
(ii)
Soft-bottomed substrates will, whenever ecologically appropriate and practical,
be sampled by core or dredge of known dimension.
(c) Other methods. Other methods may be used
where ecologically appropriate and practical.
(d) Classification attainment decisions.
Classification attainment decisions may be based on a determination of the
degree to which the sampled site conforms to the narrative aquatic life
classification criteria provided in statutory standards for water quality
classification. The decision is based on established principles of water
pollution biology and must be fully documented.
(e) Site specific impact decisions. Site
specific impact decisions may rely on established methods of analysis of
comparative data between a test community and an approved reference
community.
(f) Determination of
detrimental impact. A determination of detrimental impact to aquatic life of a
test community without an approved reference community may be made if it can be
documented, based on established methods of the interpretation of
macroinvertebrate data, and based on established principles of water pollution
biology, that the community fails to demonstrate the ecological attributes of
its designated class as defined by the narrative aquatic life standards in the
water quality classification law.
